Technology and innovation are important in addressing complex problems in the agricultural sector in many developing communities. However, ways and mechanisms to integrate them in the agricultural sector are still a challenge due to the lack of clear pathways and trajectories. Value chains are seen as a strong policy instrument to increase profitability in the agricultural sector; there is also debate around whether value chains can be a potential option to organize technology and innovation trajectories in agriculture.

Participatory research can improve the efficiency, effectiveness, and scope of research processes, and foster social inclusion, empowerment, and sustainability. Yet despite four decades of agricultural research institutions exploring and developing methods for participatory research, it has never become mainstream in the agricultural technology development cycle. Citizen science promises an innovative approach to participation in research, using the unique facilities of new digital technologies, but its potential in agricultural research participation has not been systematically probed.

Private sector actors bring expertise, resources, and new perspectives to agricultural development, but the tendency to short-term approaches and market-based orientation has been unable to drive a systemic change in the development agenda. We explore how multi-stakeholder dialogues can capitalize on and trickle systemic change through private sector involvement. Analysis from the farmer-led irrigation development multi-stakeholder dialogue space (FLI-MDS) in Ghana shows the need for a physical and institutional space to cater for and merge different stakeholder interests.

Participation of actors is essential for achievement of the United Nation’s (UN) Sustainable Development Goals (SDGs). With respect to sustainable agriculture the UN has introduced a collaborative framework for food systems transformation encompassing: 1) food system champions identification; 2) food systems assessment; 3) multi-stakeholder dialogue and action facilitation; and, 4) strengthen institutional capacity for food systems governance. The last two actions are the focus of this thesis.

Policy brief No. 1. In recent years, food consumers have become in- creasingly aware of and concerned about the sa- fety of food products. As a response, public and private actors have introduced different standards to ensure that food safety reaches the degree de- manded by consumers. Developing countries often lack the institutional capacities and financial and non-financial resources to comply with standards.

There is increasing demand for institutional reform in the agricultural sciences. This paper presents lessons from the content and directions in soil science research in India, to make a case for institutional reform in the agricultural sciences. It demonstrates how existing institutional and organizational contexts shape the research content of the soil sciences and its sub-disciplines. These contexts also shape the capacity of the soil sciences to understand and partner with other components of the wider natural resource management (NRM) innovation systems.

The Guyana School of Agriculture (GSA) was established in the year 1963 by former President Dr. Cheddi Jagan. It became a state corporation in 1964 offering the Diploma in Agriculture and the Certificate in Agriculture Programmes, and graduated the first batch of 15 students in 1966. Agricultural diversification has become an important mechanism for economic growth by providing opportunities that facilitate technological advancement and demand driven government policy.

Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of themost challenging problems in agriculture. If the diseases are not identified in the early stages, then they may ad-versely affect the total yield, resulting in a decrease in the farmers' profits. To overcome this problem, many re-searchers have presented different state-of-the-art systems based on Deep Learning and Machine Learningapproaches. However, most of these systems either use millions of training parameters or have low classificationaccuracies.